import pandas as pd
import numpy as np
from datetime import timedelta
import datetime as dt
np.seterr(divide='ignore', invalid='ignore')
import plotly.express as px
import plotly.graph_objects as go
from ipyleaflet import *
import pycountry

#Create reference to CSV file
csv_path = "Resources/H1N1_2009.csv"
csv_path_2 = "Resources/covid_19_data.csv"

#Impor the CSV into a pandas DataFrame
h1n1 = pd.read_csv(csv_path, parse_dates=["Update Time"], encoding = 'unicode_escape')
covid = pd.read_csv(csv_path_2, parse_dates=["ObservationDate"])

#location_df
covid

covid = covid.loc[:,['ObservationDate', 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']]

#Rename Columns
covid = covid.rename(columns={"ObservationDate": "Date"})

covid = covid[['Province/State', 'Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]

covid

temp = covid

#Group Provinces and take largest cumulative confirmed and death number
province_df = temp.groupby(by='Province/State').agg('max').reset_index(drop=False)

#Group all provinces into their countries and add confirmed and death numbers
province_df = province_df.groupby(by='Country/Region').agg('sum').reset_index(drop=False)


province_df

#Confirmed, Deaths and Recovered are cumulative numbers per Province/State NOT by Country/Region

#Remove countries that are in province_df dataset
remove_list = province_df['Country/Region']
country_data = temp[~temp['Country/Region'].isin(remove_list)]

#province_df
country_data = country_data.loc[:,['Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]
country_data = country_data.reset_index(drop=True)
country_data

#Merge province and country data
complete_data = pd.concat([country_data, province_df], ignore_index=True)

complete_data

covid_2 = pd.read_csv(csv_path_2, parse_dates=["ObservationDate"])

covid_2 = covid_2.loc[:,['ObservationDate', 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']]

#Rename Columns
covid_2 = covid_2.rename(columns={"ObservationDate": "Date", "Province/State" : "Province", "Country/Region" : "Country"})

covid_2 = covid_2.groupby(['Date', 'Country'])[["Confirmed", "Deaths", "Recovered"]].sum().reset_index()

covid_2

Days Since 1st Case & Weeks Since 1st Case¶

Infected Countries Ordered by Date Since 1st Confirmed Case¶

#Create Dataset that lists all countries
dfww = covid.loc[:,['Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]

#Create DataFrame of days with Confirmed Cases
df1 = dfww[dfww['Confirmed']>0]
df1 = df1.reset_index(drop=True)

#Find start date of infections in YYY-DD-MM format
start_day = df1.groupby(by='Country/Region').agg('min').reset_index(drop=False)
start_day = start_day.loc[:,['Country/Region', 'Date']]

#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()

#Days since first infection date
days_difference = []
days_difference = (today - start_day['Date']).dt.days
days_difference

#Merge on index
start_day = start_day.merge(days_difference, left_index=True, right_index=True)
start_day = start_day.rename(columns={'Country/Region': 'Country/Region', 
                                      'Date_x': 'Start Date', 
                                      'Date_y': 'Days Since 1st Case'})

start_day['Weeks Since 1st Case'] = round(start_day['Days Since 1st Case'] / 7, 2)

start_day.sort_values(by='Days Since 1st Case', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='Reds')
#start_day

start_day.sort_values(by='Days Since 1st Case', ascending=True)\
.reset_index(drop=True).style.background_gradient(cmap='Reds')

#Store Country, Days Since 1st Case and Weeks Since 1st Case
start_day = start_day.loc[:, ['Country/Region', 'Days Since 1st Case', 'Weeks Since 1st Case']]
start_day

Country Comparisons¶

Confirmed Cases desc. by Country¶

#Merge datasets together so age metrics follow global analysis
df5 = complete_data.groupby(by='Country/Region').agg('max').reset_index(drop=False)

df5 = pd.merge(df5, start_day, on='Country/Region')

#Add Column for "Active" = 'Confirmed' - 'Deaths' - 'Recovered'

active = df5['Confirmed'] - df5['Deaths'] - df5['Recovered']

df5['Active'] = active

df5 = df5[['Country/Region', 'Date', 
             'Confirmed', 'Deaths', 
             'Recovered', 'Active',
             'Date', 'Days Since 1st Case', 
             'Weeks Since 1st Case'
            ]]

df5 = df5.loc[:,['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'Days Since 1st Case', 'Weeks Since 1st Case']]
df5

Confirmed Cases by Country¶

dfww_confirmed = df5.sort_values(by='Confirmed', ascending=False).reset_index(drop=True)

dfww_confirmed.style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})

#dfww_confirmed

Confirmed Deaths by Country¶

dfww_deaths = df5.sort_values(by='Deaths', ascending=False).reset_index(drop=True)

dfww_deaths.style.background_gradient(cmap='Reds').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})

Recovered Cases by Country¶

dfww_recovered = df5.sort_values(by='Recovered', ascending=False).reset_index(drop=True)

dfww_recovered.style.background_gradient(cmap='Greens').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})

Active Cases by Country¶

dfww_active = df5.sort_values(by='Active', ascending=False).reset_index(drop=True)

dfww_active.style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})

Recovery Percent by Country (Recovered / Confirmed Cases)¶

dfww_recovered['Recovered Percent'] = round((dfww_recovered['Recovered'] / dfww_recovered['Confirmed']), 4)

dfww_recovered = dfww_recovered[['Country/Region', 
                                 'Confirmed', 'Deaths', 'Recovered', 
                                 'Active', 'Recovered Percent', 
                                 'Days Since 1st Case', 'Weeks Since 1st Case']]

dfww_recovered = dfww_recovered.loc[dfww_recovered['Confirmed'] > 2000, :]

dfww_recovered.sort_values(by='Recovered Percent', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='Greens').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Recovered Percent': '{:.2%}', 'Weeks Since 1st Case': '{:.2f}'})

Mortality Rate by Country¶

dfww_deaths['Mortality Rate'] = round((dfww_deaths['Deaths'] / dfww_deaths['Confirmed']), 4)

dfww_deaths = dfww_deaths[['Country/Region', 
                                 'Confirmed', 'Deaths', 'Recovered', 
                                 'Active', 'Mortality Rate', 
                                 'Days Since 1st Case', 'Weeks Since 1st Case']]

dfww_deaths = dfww_deaths.loc[dfww_deaths['Confirmed'] > 2000, :]

dfww_deaths.sort_values(by='Mortality Rate', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Mortality Rate': '{:.02%}', 'Weeks Since 1st Case': '{:.2f}'})

Country Deep Dive¶

Italy¶

italy = covid.loc[covid["Country/Region"] == "Italy", :]
italy = italy.reset_index(drop=True)


#Create DataFrame of days with Confirmed Cases
italy_temp = italy[italy['Confirmed']>0]
italy_temp = italy_temp.reset_index(drop=True)

#Find start date of infections in YYY-DD-MM format
italy_start = italy_temp['Date'].reset_index(drop=True)[0]

#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()

#Days since first infection date
difference = today - italy_start
difference = difference.days
difference

#Find Daily increase in Confirmed, Deaths, and Recovered
it_confirmed_pct_change = round(italy['Confirmed'].pct_change() * 100, 2)
it_death_change = round(italy['Deaths'].pct_change() * 100, 2)
it_recovery_change = round(italy['Recovered'].pct_change() * 100, 2)

#Find Daily Percentage Increase in Confirmed, Deaths, and Recovered
it_confirmed_perday = italy['Confirmed'].diff()
it_death_perday = italy['Deaths'].diff()
it_recovered_perday = italy['Recovered'].diff()


italy_percent_change = pd.DataFrame

italy_percent = pd.DataFrame({
                   "New Confirmed Cases Per Day": it_confirmed_perday,
                   "Confirmed Percent Change" : it_confirmed_pct_change,
                   "New Confirmed Deaths Per Day": it_death_perday,
                   "Death Percent Change" : it_death_change,
                   "New Recovered Cases Per Day": it_recovered_perday,
                   "Recovery Percent Change" : it_recovery_change,
                    })
    
italy_percent.fillna(value=0)

#Merge DataSets together into 1 df
italy_df = italy.merge(italy_percent, left_index=True, right_index=True)
italy_df = italy_df.fillna(value=0)

italy_df

Italy - Confirmed Deaths by Day¶

fig = px.bar(italy_df, x='Date', y='New Confirmed Deaths Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Deaths Per Day',
             height=400)
fig.show()

Italy - New Confirmed Cases Per Day¶

fig = px.bar(italy_df, x='Date', y='New Confirmed Cases Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Cases Per Day',
             height=400)
fig.show()

us = covid.loc[covid["Country/Region"] == "US", :]
us = us.reset_index(drop=True)

us

#Create DataFrame of days with Confirmed Cases
us_temp = us[us['Confirmed']>0]
us_temp = us_temp.reset_index(drop=True)

United States¶

us = covid.loc[covid["Country/Region"] == "US", :]
us = us.reset_index(drop=True)

#Group by Date and not state
us_df = us.groupby(by="Date").agg('sum').reset_index(drop=False)


#Create DataFrame of days with Confirmed Cases
us_temp = us_df[us_df['Confirmed']>0]
us_temp = us_temp.reset_index(drop=True)

#Find start date of infections in YYY-DD-MM format
us_start = us_temp['Date'].reset_index(drop=True)[0]

#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()

#Days since first infection date
difference = today - us_start
difference = difference.days
difference

#Find Daily increase in Confirmed, Deaths, and Recovered
us_confirmed_pct_change = round(us_df['Confirmed'].pct_change() * 100, 2)
us_death_change = round(us_df['Deaths'].pct_change() * 100, 2)
us_recovery_change = round(us_df['Recovered'].pct_change() * 100, 2)

#Find Daily Percentage Increase in Confirmed, Deaths, and Recovered
us_confirmed_perday = us_df['Confirmed'].diff()
us_death_perday = us_df['Deaths'].diff()
us_recovered_perday = us_df['Recovered'].diff()


us_percent_change = pd.DataFrame

us_percent = pd.DataFrame({
                   "New Confirmed Cases Per Day": us_confirmed_perday,
                   "Confirmed Percent Change" : us_confirmed_pct_change,
                   "New Confirmed Deaths Per Day": us_death_perday,
                   "Death Percent Change" : us_death_change,
                   "New Recovered Cases Per Day": us_recovered_perday,
                   "Recovery Percent Change" : us_recovery_change,
                    })
    
us_percent.fillna(value=0)

#Merge DataSets together into 1 df
us_df = us_df.merge(us_percent, left_index=True, right_index=True)
us_df = us_df.fillna(value=0)

New Confirmed Cases Per Day¶

fig = px.bar(us_df, x='Date', y='New Confirmed Cases Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Cases Per Day',
             height=400)
fig.show()

New Confirmed Deaths Per Day¶

fig = px.bar(us_df, x='Date', y='New Confirmed Deaths Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Deaths Per Day',
             height=400)
fig.show()

Recovered Cases Per Day¶

fig = px.bar(us_df, x='Date', y='New Recovered Cases Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Recovered Cases Per Day',
             height=400)
fig.show()

Confirmed Percent Change¶

fig = px.bar(us_df, x='Date', y='Confirmed Percent Change',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Confirmed Percent Change',
             height=400)
fig.show()

us_df

fig = px.bar(us_df, x='Date', y='Death Percent Change',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Death Percent Change',
             height=400)
fig.show()

 fig = px.bar(us_df, x='Date', y='Death Percent Change',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Death Percent Change',
             height=400)
fig.show()

covid_2['Date'] = covid_2['Date'].dt.strftime('%Y-%m-%d')

Confirmed Cases by Country¶

fig = px.area(covid_2, x="Date", y="Confirmed", color="Country", line_group="Country")
fig.show()

fig = px.area(covid_2, x="Date", y="Deaths", color="Country", line_group="Country")
fig.show()

# covid_2['Date'] = covid_2['Date'].dt.strftime('%Y-%m-%d')

#ADD ISO_CODES for Choropleth
input_countries = covid_2['Country']

countries = {}
for country in pycountry.countries:
    countries[country.name] = country.alpha_3

codes = [countries.get(country, 'Unknown code') for country in input_countries]

#turn into dataframe
codes_df = pd.DataFrame(codes)

#concat
covid_iso = pd.concat([covid_2, codes_df], axis=1)

#Clean-up
covid_iso = covid_iso.rename(columns={0: 'iso_code'})

covid_iso.sort_values(by='Confirmed', ascending=False)

covid_iso.loc[covid_iso['Country'] == 'US' , 'iso_code'] = 'USA'
covid_iso.loc[covid_iso['Country'] == 'Mainland China' , 'iso_code'] = 'CHN'
covid_iso.loc[covid_iso['Country'] == 'UK' , 'iso_code'] = 'GBR'
covid_iso.loc[covid_iso['Country'] == 'Russia', 'iso_code'] = 'RUS'
covid_iso.loc[covid_iso['Country'] == 'South Korea', 'iso_code'] = 'KOR'
covid_iso.loc[covid_iso['Country'] == 'Macau', 'iso_code'] = 'MAC'
covid_iso.loc[covid_iso['Country'] == 'Taiwan', 'iso_code'] = 'TWN'
covid_iso.loc[covid_iso['Country'] == 'Venezuela', 'iso_code'] = 'VEN'
covid_iso.loc[covid_iso['Country'] == 'Vietnam', 'iso_code'] = 'VNM'
covid_iso.loc[covid_iso['Country'] == 'Syria', 'iso_code'] = 'SYR'
covid_iso.loc[covid_iso['Country'] == 'Tanzania', 'iso_code'] = 'TZA'
covid_iso.loc[covid_iso['Country'] == 'Kosovo', 'iso_code'] = 'RKS'
covid_iso.loc[covid_iso['Country'] == 'West Bank and Gaza', 'iso_code'] = 'PSE'
covid_iso.loc[covid_iso['Country'] == 'Iran', 'iso_code'] = 'IRN'

covid_iso.sort_values(by='Confirmed', ascending=False)


try:
    covid_iso['Mortality Rate'] = round(covid_iso['Deaths'] / covid_iso['Confirmed'], 2) *100
except ZeroDivisionError:
    covid_iso['Mortality Rate'] = 0
    

covid_iso[covid_iso['iso_code'] == 'Unknown code']

Confirmed Numbers by Country TimeLapse¶

fig = px.choropleth(covid_iso, locations="iso_code", color="Confirmed", hover_name="Country", animation_frame="Date", range_color=[0,150000])
fig.show()

Mortality Rate by Country TimeLapse¶

fig = px.choropleth(covid_iso, locations="iso_code", color="Mortality Rate", hover_name="Country", animation_frame="Date", range_color=[0,8])
fig.show()

United States by State Data¶

usa = covid[covid['Country/Region'] == 'US'].reset_index(drop=True)
usa = usa.rename(columns={'Province/State': 'State', 
                          'Country/Region': 'Country'})

usa

	SNo	ObservationDate	Province/State	Country/Region	Last Update	Confirmed	Deaths	Recovered
0	1	2020-01-22	Anhui	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
1	2	2020-01-22	Beijing	Mainland China	1/22/2020 17:00	14.0	0.0	0.0
2	3	2020-01-22	Chongqing	Mainland China	1/22/2020 17:00	6.0	0.0	0.0
3	4	2020-01-22	Fujian	Mainland China	1/22/2020 17:00	1.0	0.0	0.0
4	5	2020-01-22	Gansu	Mainland China	1/22/2020 17:00	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...
18641	18642	2020-04-25	Wyoming	US	2020-04-26 02:31:18	491.0	7.0	0.0
18642	18643	2020-04-25	Xinjiang	Mainland China	2020-04-26 02:31:18	76.0	3.0	73.0
18643	18644	2020-04-25	Yukon	Canada	2020-04-26 02:31:18	11.0	0.0	0.0
18644	18645	2020-04-25	Yunnan	Mainland China	2020-04-26 02:31:18	185.0	2.0	181.0
18645	18646	2020-04-25	Zhejiang	Mainland China	2020-04-26 02:31:18	1268.0	1.0	1257.0

	Province/State	Country/Region	Date	Confirmed	Deaths	Recovered
0	Anhui	Mainland China	2020-01-22	1.0	0.0	0.0
1	Beijing	Mainland China	2020-01-22	14.0	0.0	0.0
2	Chongqing	Mainland China	2020-01-22	6.0	0.0	0.0
3	Fujian	Mainland China	2020-01-22	1.0	0.0	0.0
4	Gansu	Mainland China	2020-01-22	0.0	0.0	0.0
...	...	...	...	...	...	...
18641	Wyoming	US	2020-04-25	491.0	7.0	0.0
18642	Xinjiang	Mainland China	2020-04-25	76.0	3.0	73.0
18643	Yukon	Canada	2020-04-25	11.0	0.0	0.0
18644	Yunnan	Mainland China	2020-04-25	185.0	2.0	181.0
18645	Zhejiang	Mainland China	2020-04-25	1268.0	1.0	1257.0

	Country/Region	Confirmed	Deaths	Recovered
0	Australia	6694.0	80.0	5271.0
1	Canada	46357.0	2565.0	14.0
2	Denmark	1524.0	13.0	190.0
3	France	35456.0	1456.0	2949.0
4	Germany	5.0	0.0	0.0
5	Hong Kong	1037.0	4.0	753.0
6	Israel	8.0	0.0	0.0
7	Lebanon	2.0	0.0	0.0
8	Macau	45.0	0.0	28.0
9	Mainland China	82827.0	4632.0	78225.0
10	Netherlands	3825.0	151.0	104.0
11	Others	61.0	0.0	0.0
12	Taiwan	47.0	1.0	17.0
13	UK	6668.0	303.0	857.0
14	US	939634.0	53786.0	101141.0

	Country/Region	Date	Confirmed	Deaths	Recovered
0	Japan	2020-01-22	2.0	0.0	0.0
1	Thailand	2020-01-22	2.0	0.0	0.0
2	South Korea	2020-01-22	1.0	0.0	0.0
3	Japan	2020-01-23	1.0	0.0	0.0
4	Thailand	2020-01-23	3.0	0.0	0.0
...	...	...	...	...	...
8940	West Bank and Gaza	2020-04-25	342.0	2.0	92.0
8941	Western Sahara	2020-04-25	6.0	0.0	5.0
8942	Yemen	2020-04-25	1.0	0.0	1.0
8943	Zambia	2020-04-25	84.0	3.0	37.0
8944	Zimbabwe	2020-04-25	31.0	4.0	2.0

	Country/Region	Date	Confirmed	Deaths	Recovered
0	Japan	2020-01-22	2.0	0.0	0.0
1	Thailand	2020-01-22	2.0	0.0	0.0
2	South Korea	2020-01-22	1.0	0.0	0.0
3	Japan	2020-01-23	1.0	0.0	0.0
4	Thailand	2020-01-23	3.0	0.0	0.0
...	...	...	...	...	...
8955	Netherlands	NaT	3825.0	151.0	104.0
8956	Others	NaT	61.0	0.0	0.0
8957	Taiwan	NaT	47.0	1.0	17.0
8958	UK	NaT	6668.0	303.0	857.0
8959	US	NaT	939634.0	53786.0	101141.0

	Country/Region	Start Date	Days Since 1st Case	Weeks Since 1st Case
0	Taiwan	2020-01-22 00:00:00	95	13.570000
1	Macau	2020-01-22 00:00:00	95	13.570000
2	Mainland China	2020-01-22 00:00:00	95	13.570000
3	Japan	2020-01-22 00:00:00	95	13.570000
4	Thailand	2020-01-22 00:00:00	95	13.570000
5	South Korea	2020-01-22 00:00:00	95	13.570000
6	US	2020-01-22 00:00:00	95	13.570000
7	Singapore	2020-01-23 00:00:00	94	13.430000
8	Vietnam	2020-01-23 00:00:00	94	13.430000
9	Hong Kong	2020-01-23 00:00:00	94	13.430000
10	France	2020-01-24 00:00:00	93	13.290000
11	Australia	2020-01-25 00:00:00	92	13.140000
12	Nepal	2020-01-25 00:00:00	92	13.140000
13	Malaysia	2020-01-25 00:00:00	92	13.140000
14	Canada	2020-01-26 00:00:00	91	13.000000
15	Cambodia	2020-01-27 00:00:00	90	12.860000
16	Sri Lanka	2020-01-27 00:00:00	90	12.860000
17	Ivory Coast	2020-01-27 00:00:00	90	12.860000
18	Germany	2020-01-28 00:00:00	89	12.710000
19	United Arab Emirates	2020-01-29 00:00:00	88	12.570000
20	Finland	2020-01-29 00:00:00	88	12.570000
21	India	2020-01-30 00:00:00	87	12.430000
22	Philippines	2020-01-30 00:00:00	87	12.430000
23	Sweden	2020-01-31 00:00:00	86	12.290000
24	Russia	2020-01-31 00:00:00	86	12.290000
25	UK	2020-01-31 00:00:00	86	12.290000
26	Italy	2020-01-31 00:00:00	86	12.290000
27	Spain	2020-02-01 00:00:00	85	12.140000
28	Belgium	2020-02-04 00:00:00	82	11.710000
29	Others	2020-02-07 00:00:00	79	11.290000
30	Egypt	2020-02-14 00:00:00	72	10.290000
31	Iran	2020-02-19 00:00:00	67	9.570000
32	Lebanon	2020-02-21 00:00:00	65	9.290000
33	Israel	2020-02-21 00:00:00	65	9.290000
34	Iraq	2020-02-24 00:00:00	62	8.860000
35	Afghanistan	2020-02-24 00:00:00	62	8.860000
36	Bahrain	2020-02-24 00:00:00	62	8.860000
37	Oman	2020-02-24 00:00:00	62	8.860000
38	Kuwait	2020-02-24 00:00:00	62	8.860000
39	Croatia	2020-02-25 00:00:00	61	8.710000
40	Algeria	2020-02-25 00:00:00	61	8.710000
41	Switzerland	2020-02-25 00:00:00	61	8.710000
42	Austria	2020-02-25 00:00:00	61	8.710000
43	Georgia	2020-02-26 00:00:00	60	8.570000
44	Brazil	2020-02-26 00:00:00	60	8.570000
45	North Macedonia	2020-02-26 00:00:00	60	8.570000
46	Norway	2020-02-26 00:00:00	60	8.570000
47	Romania	2020-02-26 00:00:00	60	8.570000
48	Pakistan	2020-02-26 00:00:00	60	8.570000
49	Greece	2020-02-26 00:00:00	60	8.570000
50	San Marino	2020-02-27 00:00:00	59	8.430000
51	Estonia	2020-02-27 00:00:00	59	8.430000
52	Denmark	2020-02-27 00:00:00	59	8.430000
53	Netherlands	2020-02-27 00:00:00	59	8.430000
54	Lithuania	2020-02-28 00:00:00	58	8.290000
55	Azerbaijan	2020-02-28 00:00:00	58	8.290000
56	Belarus	2020-02-28 00:00:00	58	8.290000
57	Iceland	2020-02-28 00:00:00	58	8.290000
58	Mexico	2020-02-28 00:00:00	58	8.290000
59	North Ireland	2020-02-28 00:00:00	58	8.290000
60	Nigeria	2020-02-28 00:00:00	58	8.290000
61	New Zealand	2020-02-28 00:00:00	58	8.290000
62	Luxembourg	2020-02-29 00:00:00	57	8.140000
63	Qatar	2020-02-29 00:00:00	57	8.140000
64	Ireland	2020-02-29 00:00:00	57	8.140000
65	Monaco	2020-02-29 00:00:00	57	8.140000
66	Ecuador	2020-03-01 00:00:00	56	8.000000
67	Dominican Republic	2020-03-01 00:00:00	56	8.000000
68	Czech Republic	2020-03-01 00:00:00	56	8.000000
69	Armenia	2020-03-01 00:00:00	56	8.000000
70	Azerbaijan	2020-03-01 00:00:00	56	8.000000
71	Senegal	2020-03-02 00:00:00	55	7.860000
72	Saudi Arabia	2020-03-02 00:00:00	55	7.860000
73	Indonesia	2020-03-02 00:00:00	55	7.860000
74	Morocco	2020-03-02 00:00:00	55	7.860000
75	Portugal	2020-03-02 00:00:00	55	7.860000
76	Latvia	2020-03-02 00:00:00	55	7.860000
77	Andorra	2020-03-02 00:00:00	55	7.860000
78	Jordan	2020-03-03 00:00:00	54	7.710000
79	Chile	2020-03-03 00:00:00	54	7.710000
80	Ukraine	2020-03-03 00:00:00	54	7.710000
81	Argentina	2020-03-03 00:00:00	54	7.710000
82	Tunisia	2020-03-04 00:00:00	53	7.570000
83	Faroe Islands	2020-03-04 00:00:00	53	7.570000
84	Hungary	2020-03-04 00:00:00	53	7.570000
85	Liechtenstein	2020-03-04 00:00:00	53	7.570000
86	Saint Barthelemy	2020-03-04 00:00:00	53	7.570000
87	Poland	2020-03-04 00:00:00	53	7.570000
88	Gibraltar	2020-03-04 00:00:00	53	7.570000
89	Bosnia and Herzegovina	2020-03-05 00:00:00	52	7.430000
90	Slovenia	2020-03-05 00:00:00	52	7.430000
91	South Africa	2020-03-05 00:00:00	52	7.430000
92	Palestine	2020-03-05 00:00:00	52	7.430000
93	Vatican City	2020-03-06 00:00:00	51	7.290000
94	Slovakia	2020-03-06 00:00:00	51	7.290000
95	Togo	2020-03-06 00:00:00	51	7.290000
96	Bhutan	2020-03-06 00:00:00	51	7.290000
97	Colombia	2020-03-06 00:00:00	51	7.290000
98	Peru	2020-03-06 00:00:00	51	7.290000
99	Cameroon	2020-03-06 00:00:00	51	7.290000
100	Serbia	2020-03-06 00:00:00	51	7.290000
101	Costa Rica	2020-03-06 00:00:00	51	7.290000
102	French Guiana	2020-03-07 00:00:00	50	7.140000
103	Martinique	2020-03-07 00:00:00	50	7.140000
104	Malta	2020-03-07 00:00:00	50	7.140000
105	Republic of Ireland	2020-03-08 00:00:00	49	7.000000
106	Bangladesh	2020-03-08 00:00:00	49	7.000000
107	Bulgaria	2020-03-08 00:00:00	49	7.000000
108	Maldives	2020-03-08 00:00:00	49	7.000000
109	Moldova	2020-03-08 00:00:00	49	7.000000
110	Paraguay	2020-03-08 00:00:00	49	7.000000
111	St. Martin	2020-03-09 00:00:00	48	6.860000
112	Cyprus	2020-03-09 00:00:00	48	6.860000
113	Albania	2020-03-09 00:00:00	48	6.860000
114	Brunei	2020-03-09 00:00:00	48	6.860000
115	Panama	2020-03-10 00:00:00	47	6.710000
116	Mongolia	2020-03-10 00:00:00	47	6.710000
117	occupied Palestinian territory	2020-03-10 00:00:00	47	6.710000
118	Burkina Faso	2020-03-10 00:00:00	47	6.710000
119	('St. Martin',)	2020-03-10 00:00:00	47	6.710000
120	Channel Islands	2020-03-10 00:00:00	47	6.710000
121	Holy See	2020-03-10 00:00:00	47	6.710000
122	Honduras	2020-03-11 00:00:00	46	6.570000
123	Congo (Kinshasa)	2020-03-11 00:00:00	46	6.570000
124	Turkey	2020-03-11 00:00:00	46	6.570000
125	Bolivia	2020-03-11 00:00:00	46	6.570000
126	Reunion	2020-03-11 00:00:00	46	6.570000
127	Jamaica	2020-03-11 00:00:00	46	6.570000
128	Cuba	2020-03-12 00:00:00	45	6.430000
129	Guyana	2020-03-12 00:00:00	45	6.430000
130	Aruba	2020-03-13 00:00:00	44	6.290000
131	Cayman Islands	2020-03-13 00:00:00	44	6.290000
132	Guinea	2020-03-13 00:00:00	44	6.290000
133	Ethiopia	2020-03-13 00:00:00	44	6.290000
134	Sudan	2020-03-13 00:00:00	44	6.290000
135	Guadeloupe	2020-03-13 00:00:00	44	6.290000
136	Kazakhstan	2020-03-13 00:00:00	44	6.290000
137	Kenya	2020-03-13 00:00:00	44	6.290000
138	Antigua and Barbuda	2020-03-13 00:00:00	44	6.290000
139	Seychelles	2020-03-14 00:00:00	43	6.140000
140	Eswatini	2020-03-14 00:00:00	43	6.140000
141	Rwanda	2020-03-14 00:00:00	43	6.140000
142	Gabon	2020-03-14 00:00:00	43	6.140000
143	Saint Lucia	2020-03-14 00:00:00	43	6.140000
144	Saint Vincent and the Grenadines	2020-03-14 00:00:00	43	6.140000
145	Ghana	2020-03-14 00:00:00	43	6.140000
146	Guatemala	2020-03-14 00:00:00	43	6.140000
147	Uruguay	2020-03-14 00:00:00	43	6.140000
148	Mauritania	2020-03-14 00:00:00	43	6.140000
149	Guernsey	2020-03-14 00:00:00	43	6.140000
150	Suriname	2020-03-14 00:00:00	43	6.140000
151	Venezuela	2020-03-14 00:00:00	43	6.140000
152	Namibia	2020-03-14 00:00:00	43	6.140000
153	Trinidad and Tobago	2020-03-14 00:00:00	43	6.140000
154	Curacao	2020-03-14 00:00:00	43	6.140000
155	Jersey	2020-03-14 00:00:00	43	6.140000
156	Equatorial Guinea	2020-03-15 00:00:00	42	6.000000
157	Uzbekistan	2020-03-15 00:00:00	42	6.000000
158	Central African Republic	2020-03-15 00:00:00	42	6.000000
159	Congo (Brazzaville)	2020-03-15 00:00:00	42	6.000000
160	Kosovo	2020-03-15 00:00:00	42	6.000000
161	Liberia	2020-03-16 00:00:00	41	5.860000
162	Benin	2020-03-16 00:00:00	41	5.860000
163	The Bahamas	2020-03-16 00:00:00	41	5.860000
164	Guam	2020-03-16 00:00:00	41	5.860000
165	Mayotte	2020-03-16 00:00:00	41	5.860000
166	Republic of the Congo	2020-03-16 00:00:00	41	5.860000
167	Tanzania	2020-03-16 00:00:00	41	5.860000
168	Somalia	2020-03-16 00:00:00	41	5.860000
169	Greenland	2020-03-16 00:00:00	41	5.860000
170	Puerto Rico	2020-03-16 00:00:00	41	5.860000
171	Montenegro	2020-03-17 00:00:00	40	5.710000
172	The Gambia	2020-03-17 00:00:00	40	5.710000
173	Barbados	2020-03-17 00:00:00	40	5.710000
174	Kyrgyzstan	2020-03-18 00:00:00	39	5.570000
175	Djibouti	2020-03-18 00:00:00	39	5.570000
176	Zambia	2020-03-18 00:00:00	39	5.570000
177	Gambia, The	2020-03-18 00:00:00	39	5.570000
178	Mauritius	2020-03-18 00:00:00	39	5.570000
179	El Salvador	2020-03-19 00:00:00	38	5.430000
180	Fiji	2020-03-19 00:00:00	38	5.430000
181	Chad	2020-03-19 00:00:00	38	5.430000
182	Nicaragua	2020-03-19 00:00:00	38	5.430000
183	Bahamas, The	2020-03-19 00:00:00	38	5.430000
184	Cabo Verde	2020-03-20 00:00:00	37	5.290000
185	Angola	2020-03-20 00:00:00	37	5.290000
186	Niger	2020-03-20 00:00:00	37	5.290000
187	Zimbabwe	2020-03-20 00:00:00	37	5.290000
188	Madagascar	2020-03-20 00:00:00	37	5.290000
189	Papua New Guinea	2020-03-20 00:00:00	37	5.290000
190	Haiti	2020-03-20 00:00:00	37	5.290000
191	Uganda	2020-03-21 00:00:00	36	5.140000
192	Cape Verde	2020-03-21 00:00:00	36	5.140000
193	Eritrea	2020-03-21 00:00:00	36	5.140000
194	East Timor	2020-03-21 00:00:00	36	5.140000
195	Timor-Leste	2020-03-22 00:00:00	35	5.000000
196	Grenada	2020-03-22 00:00:00	35	5.000000
197	Mozambique	2020-03-22 00:00:00	35	5.000000
198	Syria	2020-03-22 00:00:00	35	5.000000
199	Dominica	2020-03-22 00:00:00	35	5.000000
200	Bahamas	2020-03-22 00:00:00	35	5.000000
201	Gambia	2020-03-22 00:00:00	35	5.000000
202	Belize	2020-03-23 00:00:00	34	4.860000
203	Libya	2020-03-24 00:00:00	33	4.710000
204	Laos	2020-03-24 00:00:00	33	4.710000
205	Diamond Princess	2020-03-25 00:00:00	32	4.570000
206	Guinea-Bissau	2020-03-25 00:00:00	32	4.570000
207	Mali	2020-03-25 00:00:00	32	4.570000
208	Saint Kitts and Nevis	2020-03-25 00:00:00	32	4.570000
209	West Bank and Gaza	2020-03-26 00:00:00	31	4.430000
210	Burma	2020-03-27 00:00:00	30	4.290000
211	MS Zaandam	2020-03-28 00:00:00	29	4.140000
212	Botswana	2020-03-30 00:00:00	27	3.860000
213	Burundi	2020-03-31 00:00:00	26	3.710000
214	Sierra Leone	2020-03-31 00:00:00	26	3.710000
215	Malawi	2020-04-02 00:00:00	24	3.430000
216	South Sudan	2020-04-05 00:00:00	21	3.000000
217	Western Sahara	2020-04-05 00:00:00	21	3.000000
218	Sao Tome and Principe	2020-04-06 00:00:00	20	2.860000
219	Yemen	2020-04-10 00:00:00	16	2.290000

	Country/Region	Days Since 1st Case	Weeks Since 1st Case
0	Azerbaijan	58	8.29
1	('St. Martin',)	47	6.71
2	Afghanistan	62	8.86
3	Albania	48	6.86
4	Algeria	61	8.71
...	...	...	...
215	Western Sahara	21	3.00
216	Yemen	16	2.29
217	Zambia	39	5.57
218	Zimbabwe	37	5.29
219	occupied Palestinian territory	47	6.71

	Country/Region	Confirmed	Deaths	Recovered	Active	Days Since 1st Case	Weeks Since 1st Case
0	US	939634	53786	101141	784707	95	13.57
1	Spain	223759	22902	95708	105149	85	12.14
2	Italy	195351	26384	63120	105847	86	12.29
3	Turkey	107773	2706	25582	79485	46	6.57
4	Iran	89328	5650	68193	15485	67	9.57
5	Mainland China	82827	4632	78225	-30	95	13.57
6	Russia	74588	681	6250	67657	86	12.29
7	Brazil	59324	4057	29160	26107	60	8.57
8	Canada	46357	2565	14	43778	91	13.00
9	Belgium	45325	6917	10417	27991	82	11.71
10	France	35456	1456	2949	31051	93	13.29
11	Switzerland	28894	1599	21300	5995	61	8.71
12	India	26283	825	5939	19519	87	12.43
13	Peru	25331	700	7797	16834	51	7.29
14	Portugal	23392	880	1277	21235	55	7.86
15	Ecuador	22719	576	1366	20777	56	8.00
16	Ireland	18561	1063	9233	8265	57	8.14
17	Sweden	18177	2192	1005	14980	86	12.29
18	Saudi Arabia	16299	136	2215	13948	55	7.86
19	Austria	15148	536	12103	2509	61	8.71
20	Mexico	13842	1305	7149	5388	58	8.29
21	Japan	13231	360	1656	11215	95	13.57
22	Chile	12858	181	6746	5931	54	7.71
23	Pakistan	12723	269	2866	9588	60	8.57
24	Singapore	12693	12	1002	11679	94	13.43
25	Poland	11273	524	2126	8623	53	7.57
26	South Korea	10728	242	8717	1769	95	13.57
27	Romania	10635	601	2890	7144	60	8.57
28	United Arab Emirates	9813	71	1887	7855	88	12.57
29	Belarus	9590	67	1573	7950	58	8.29
30	Qatar	9358	10	929	8419	57	8.14
31	Indonesia	8607	720	1042	6845	55	7.86
32	Ukraine	8125	201	782	7142	54	7.71
33	Norway	7499	201	32	7266	60	8.57
34	Czech Republic	7352	218	2453	4681	56	8.00
35	Philippines	7294	494	792	6008	87	12.43
36	Australia	6694	80	5271	1343	92	13.14
37	UK	6668	303	857	5508	86	12.29
38	Serbia	6630	125	870	5635	51	7.29
39	Dominican Republic	5926	273	822	4831	56	8.00
40	Malaysia	5742	98	3762	1882	92	13.14
41	Panama	5538	159	338	5041	47	6.71
42	Colombia	5142	233	1067	3842	51	7.29
43	Bangladesh	4998	140	113	4745	49	7.00
44	Finland	4475	186	2500	1789	88	12.57
45	South Africa	4361	86	1473	2802	52	7.43
46	Egypt	4319	307	1114	2898	72	10.29
47	Morocco	3897	159	537	3201	55	7.86
48	Netherlands	3825	151	104	3570	59	8.43
49	Argentina	3780	185	1030	2565	54	7.71
50	Luxembourg	3711	85	3088	538	57	8.14
51	Moldova	3304	94	825	2385	49	7.00
52	Algeria	3256	419	1479	1358	61	8.71
53	Thailand	2907	51	2547	309	95	13.57
54	Kuwait	2892	19	656	2217	62	8.86
55	Kazakhstan	2601	25	646	1930	44	6.29
56	Bahrain	2588	8	1160	1420	62	8.86
57	Greece	2506	130	577	1799	60	8.57
58	Hungary	2443	262	458	1723	53	7.57
59	Croatia	2016	54	1034	928	61	8.71
60	Oman	1905	10	329	1566	62	8.86
61	Uzbekistan	1862	8	707	1147	42	6.00
62	Iceland	1790	10	1570	210	58	8.29
63	Iraq	1763	86	1224	453	62	8.86
64	Armenia	1677	28	803	846	56	8.00
65	Estonia	1635	46	228	1361	59	8.43
66	Azerbaijan	1617	21	1080	516	56	8.00
67	Denmark	1524	13	190	1321	59	8.43
68	Cameroon	1518	53	697	768	51	7.29
69	Bosnia and Herzegovina	1486	57	592	837	52	7.43
70	New Zealand	1470	18	1142	310	58	8.29
71	Afghanistan	1463	47	188	1228	62	8.86
72	Lithuania	1426	41	460	925	58	8.29
73	Slovenia	1388	81	219	1088	52	7.43
74	Slovakia	1373	17	386	970	51	7.29
75	North Macedonia	1367	59	374	934	60	8.57
76	Cuba	1337	51	437	849	45	6.43
77	Ghana	1279	10	134	1135	43	6.14
78	Bulgaria	1247	55	197	995	49	7.00
79	Nigeria	1182	35	222	925	58	8.29
80	Ivory Coast	1077	14	419	644	90	12.86
81	Hong Kong	1037	4	753	280	94	13.43
82	Djibouti	1008	2	373	633	39	5.57
83	Guinea	996	7	208	781	44	6.29
84	Tunisia	939	38	207	694	53	7.57
85	Bolivia	866	46	54	766	46	6.57
86	Cyprus	810	14	148	648	48	6.86
87	Latvia	804	12	267	525	55	7.86
88	Andorra	738	40	344	354	55	7.86
89	Albania	712	27	403	282	48	6.86
90	Diamond Princess	712	13	645	54	32	4.57
91	Costa Rica	693	6	242	445	51	7.29
92	Niger	684	27	325	332	37	5.29
93	Kyrgyzstan	665	8	345	312	39	5.57
94	Burkina Faso	629	41	442	146	47	6.71
95	Honduras	627	59	65	503	46	6.57
96	Senegal	614	7	276	331	55	7.86
97	Uruguay	596	14	370	212	43	6.14
98	San Marino	513	40	64	409	59	8.43
99	Kosovo	510	12	93	405	42	6.00
100	West Bank and Gaza	484	4	92	388	31	4.43
101	Guatemala	473	13	45	415	43	6.14
102	Sri Lanka	460	7	118	335	90	12.86
103	Georgia	456	5	139	312	60	8.57
104	Malta	448	4	249	195	50	7.14
105	Jordan	444	7	332	105	54	7.71
106	Congo (Kinshasa)	416	28	49	339	46	6.57
107	Somalia	390	18	8	364	41	5.86
108	Mali	370	21	91	258	32	4.57
109	Kenya	343	14	98	231	44	6.29
110	Mauritius	331	9	295	27	39	5.57
111	Venezuela	323	10	132	181	43	6.14
112	Montenegro	320	6	153	161	40	5.71
113	Jamaica	305	7	28	270	46	6.57
114	Tanzania	299	10	48	241	41	5.86
115	El Salvador	274	8	75	191	38	5.43
116	Vietnam	270	0	225	45	94	13.43
117	Equatorial Guinea	258	1	7	250	42	6.00
118	Paraguay	228	9	85	134	49	7.00
119	Sudan	213	17	19	177	44	6.29
120	Congo (Brazzaville)	200	6	19	175	42	6.00
121	Rwanda	183	0	88	95	43	6.14
122	Maldives	177	0	17	160	49	7.00
123	Gabon	176	3	30	143	43	6.14
124	Burma	146	5	10	131	30	4.29
125	Brunei	138	1	121	16	48	6.86
126	Madagascar	123	0	62	61	37	5.29
127	Cambodia	122	0	117	5	90	12.86
128	Ethiopia	122	3	29	90	44	6.29
129	Liberia	120	11	25	84	41	5.86
130	Trinidad and Tobago	115	8	53	54	43	6.14
131	Togo	96	6	62	28	51	7.29
132	Monaco	94	4	42	48	57	8.14
133	Cabo Verde	90	1	1	88	37	5.29
134	Zambia	84	3	37	44	39	5.57
135	Sierra Leone	82	2	10	70	26	3.71
136	Liechtenstein	81	1	55	25	53	7.57
137	Barbados	79	6	31	42	40	5.71
138	Bahamas	78	11	15	52	35	5.00
139	Uganda	75	0	46	29	36	5.14
140	Guyana	73	7	12	54	45	6.43
141	Haiti	72	6	6	60	37	5.29
142	Mozambique	70	0	12	58	35	5.00
143	Others	61	0	0	61	79	11.29
144	Libya	61	2	18	41	33	4.71
145	Eswatini	56	1	10	45	43	6.14
146	Benin	54	1	27	26	41	5.86
147	Guadeloupe	53	0	0	53	44	6.29
148	Guinea-Bissau	52	0	3	49	32	4.57
149	Nepal	49	0	12	37	92	13.14
150	Taiwan	47	1	17	29	95	13.57
151	Chad	46	0	15	31	38	5.43
152	Macau	45	0	28	17	95	13.57
153	Reunion	45	0	0	45	46	6.57
154	Syria	42	3	11	28	35	5.00
155	Eritrea	39	0	13	26	36	5.14
156	Mongolia	37	0	9	28	47	6.71
157	Malawi	33	3	4	26	24	3.43
158	Martinique	32	1	0	31	50	7.14
159	Zimbabwe	31	4	2	25	37	5.29
160	occupied Palestinian territory	25	0	0	25	47	6.71
161	Angola	25	2	6	17	37	5.29
162	Antigua and Barbuda	24	3	11	10	44	6.29
163	Timor-Leste	24	0	2	22	35	5.00
164	Botswana	22	1	0	21	27	3.86
165	Palestine	22	0	0	22	52	7.43
166	Republic of Ireland	21	0	0	21	49	7.00
167	Laos	19	0	7	12	33	4.71
168	French Guiana	18	0	6	12	50	7.14
169	Grenada	18	0	7	11	35	5.00
170	Fiji	18	0	10	8	38	5.43
171	Belize	18	2	5	11	34	4.86
172	Namibia	16	0	7	9	43	6.14
173	Dominica	16	0	13	3	35	5.00
174	Central African Republic	16	0	10	6	42	6.00
175	Saint Lucia	15	0	15	0	43	6.14
176	Saint Kitts and Nevis	15	0	2	13	32	4.57
177	Saint Vincent and the Grenadines	14	0	5	9	43	6.14
178	Nicaragua	12	3	7	2	38	5.43
179	Burundi	11	1	4	6	26	3.71
180	Seychelles	11	0	6	5	43	6.14
181	Gambia	10	1	8	1	35	5.00
182	Suriname	10	1	7	2	43	6.14
183	Holy See	9	0	2	7	47	6.71
184	MS Zaandam	9	2	0	7	29	4.14
185	Israel	8	0	0	8	65	9.29
186	Papua New Guinea	8	0	0	8	37	5.29
187	Bhutan	7	0	3	4	51	7.29
188	Mauritania	7	1	6	0	43	6.14
189	Mayotte	7	0	0	7	41	5.86
190	Western Sahara	6	0	5	1	21	3.00
191	South Sudan	5	0	0	5	21	3.00
192	Germany	5	0	0	5	89	12.71
193	Bahamas, The	4	0	0	4	38	5.43
194	Sao Tome and Principe	4	0	0	4	20	2.86
195	Aruba	4	0	0	4	44	6.29
196	Puerto Rico	3	0	0	3	41	5.86
197	Guam	3	0	0	3	41	5.86
198	Saint Barthelemy	3	0	0	3	53	7.57
199	St. Martin	2	0	0	2	48	6.86
200	Faroe Islands	2	0	0	2	53	7.57
201	Lebanon	2	0	0	2	65	9.29
202	('St. Martin',)	2	0	0	2	47	6.71
203	Jersey	2	0	0	2	43	6.14
204	Republic of the Congo	1	0	0	1	41	5.86
205	North Ireland	1	0	0	1	58	8.29
206	Yemen	1	0	1	0	16	2.29
207	Vatican City	1	0	0	1	51	7.29
208	Guernsey	1	0	0	1	43	6.14
209	The Bahamas	1	0	0	1	41	5.86
210	The Gambia	1	0	0	1	40	5.71
211	Cape Verde	1	0	0	1	36	5.14
212	Cayman Islands	1	0	0	1	44	6.29
213	Greenland	1	0	0	1	41	5.86
214	Channel Islands	1	0	0	1	47	6.71
215	Curacao	1	0	0	1	43	6.14
216	Gibraltar	1	0	1	0	53	7.57
217	East Timor	1	0	0	1	36	5.14
218	Gambia, The	1	0	0	1	39	5.57
219	Azerbaijan	1	0	0	1	58	8.29

	New Confirmed Cases Per Day	Confirmed Percent Change	New Confirmed Deaths Per Day	Death Percent Change	New Recovered Cases Per Day	Recovery Percent Change
0	0.0	0.00	0.0	0.00	0.0	0.00
1	0.0	0.00	0.0	0.00	0.0	0.00
2	0.0	0.00	0.0	0.00	0.0	0.00
3	0.0	0.00	0.0	0.00	0.0	0.00
4	0.0	0.00	0.0	0.00	0.0	0.00
...	...	...	...	...	...	...
81	2729.0	1.51	534.0	2.21	2723.0	5.57
82	3370.0	1.83	437.0	1.77	2943.0	5.70
83	2646.0	1.41	464.0	1.85	3033.0	5.56
84	3021.0	1.59	420.0	1.64	2922.0	5.08
85	2357.0	1.22	415.0	1.60	2622.0	4.33

	Province/State	Country/Region	Date	Confirmed	Deaths	Recovered	New Confirmed Cases Per Day	Confirmed Percent Change	New Confirmed Deaths Per Day	Death Percent Change	New Recovered Cases Per Day	Recovery Percent Change
0	0	Italy	2020-01-31	2.0	0.0	0.0	0.0	0.00	0.0	0.00	0.0	0.00
1	0	Italy	2020-02-01	2.0	0.0	0.0	0.0	0.00	0.0	0.00	0.0	0.00
2	0	Italy	2020-02-02	2.0	0.0	0.0	0.0	0.00	0.0	0.00	0.0	0.00
3	0	Italy	2020-02-03	2.0	0.0	0.0	0.0	0.00	0.0	0.00	0.0	0.00
4	0	Italy	2020-02-04	2.0	0.0	0.0	0.0	0.00	0.0	0.00	0.0	0.00
...	...	...	...	...	...	...	...	...	...	...	...	...
81	0	Italy	2020-04-21	183957.0	24648.0	51600.0	2729.0	1.51	534.0	2.21	2723.0	5.57
82	0	Italy	2020-04-22	187327.0	25085.0	54543.0	3370.0	1.83	437.0	1.77	2943.0	5.70
83	0	Italy	2020-04-23	189973.0	25549.0	57576.0	2646.0	1.41	464.0	1.85	3033.0	5.56
84	0	Italy	2020-04-24	192994.0	25969.0	60498.0	3021.0	1.59	420.0	1.64	2922.0	5.08
85	0	Italy	2020-04-25	195351.0	26384.0	63120.0	2357.0	1.22	415.0	1.60	2622.0	4.33

	Date	Country	Confirmed	Deaths	Recovered	iso_code	Mortality Rate
69	2020-01-27	Ivory Coast	1.0	0.0	0.0	Unknown code	0.0
348	2020-02-07	Others	61.0	0.0	0.0	Unknown code	0.0
377	2020-02-08	Others	61.0	0.0	0.0	Unknown code	0.0
406	2020-02-09	Others	64.0	0.0	0.0	Unknown code	0.0
435	2020-02-10	Others	135.0	0.0	0.0	Unknown code	0.0
...	...	...	...	...	...	...	...
10051	2020-04-25	Holy See	9.0	0.0	2.0	Unknown code	0.0
10063	2020-04-25	Ivory Coast	1077.0	14.0	419.0	Unknown code	1.0
10072	2020-04-25	Laos	19.0	0.0	7.0	Unknown code	0.0
10080	2020-04-25	MS Zaandam	9.0	2.0	0.0	Unknown code	22.0
10092	2020-04-25	Moldova	3304.0	94.0	825.0	Unknown code	3.0

	State	Country	Date	Confirmed	Deaths	Recovered
0	Washington	US	2020-01-22	1.0	0.0	0.0
1	Washington	US	2020-01-23	1.0	0.0	0.0
2	Washington	US	2020-01-24	1.0	0.0	0.0
3	Chicago	US	2020-01-24	1.0	0.0	0.0
4	Washington	US	2020-01-25	1.0	0.0	0.0
...	...	...	...	...	...	...
3593	Virginia	US	2020-04-25	12366.0	437.0	0.0
3594	Washington	US	2020-04-25	13319.0	737.0	0.0
3595	West Virginia	US	2020-04-25	1010.0	32.0	0.0
3596	Wisconsin	US	2020-04-25	5687.0	266.0	0.0
3597	Wyoming	US	2020-04-25	491.0	7.0	0.0